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Evaluating the Performance of the OWC Satellite System Under Different Parameters Using the Optisystem Program

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Evaluating the Performance of the OWC Satellite System Under...

Conference of Recent Trends and Applications of Soft Computing in Engineering, RTASCE 2024

作者： Abood, Layla H. Al-Hilali, Aqeel Ali Galeb, Basim Saber, Ali Ali Bashar, Mustafa Department of Control and Systems Engineering University of Technology Baghdad Iraq Medical Instrumentation Engineering Department Al-Farahidi University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Cybersecurity Technology Engineering Department Al-Kitab University Kirkuk Iraq Medical Instrumentation Engineering Department Uruk University Baghdad Iraq

ISBN: (纸本)9783031828805

The potential of optical wireless communication (OWC) systems for high-speed data transfer, especially over extended distances, is being investigated more and more. However, problems including signal attenuation, dispersion, and noise pose a serious challenge to these systems’ effectiveness as transmission lengths increase. This research examines how an OWC system built using OptiSystem software is affected by different transmission lengths, ranging from 1000 to 3000 km. The results of the investigation show that the Quality Factor (QF) and the Bit Error Rate (BER) have a significant negative connection, with more signal distortion and attenuation occurring at greater distances. While rising noise power—due to attenuation and compensatory noise from optical amplifiers—further degrades signal quality by reducing the Signal-To-Noise Ratio (SNR) and Optical Signal-To-Noise Ratio (OSNR), the eye diagram's narrowing indicates increased pulse broadening and intersymbol interference. Furthermore, timing irregularities, as measured by RMS jitter and peak-to-peak jitter, became larger at greater distances, indicating the detrimental effects of attenuation on signal accuracy. The exponential increase in BER logarithm with distance highlights the increasing error rates. These results highlight the serious problems with long-distance transmission and the need for creative solutions. To further enhance the performance and dependability of high-speed OWC systems over long distances, future research should concentrate on creating sophisticated dispersion compensation algorithms, improving noise control tactics, and investigating adaptive modulation schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Timing jitter

Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

A Real-time Hand Gesture Recognition System for Human-computer and Human-Robot Interaction

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A Real-time Hand Gesture Recognition System for Human-Comput...

2022 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2022

作者： Ponzi, Valerio Iacobelli, Emanuele Napoli, Christian Starczewski, Janusz Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

The proposed hand gesture recognition (HGR) system is designed to enhance human-computer interaction (HCI) and human-robot interaction (HRI), which are crucial areas of research aimed at improving the way humans interact with computer or robot systems. With the growing need for intelligent computers and robots in a range of applications, including healthcare, manufacturing, and education, both HCI and HRI have gained significant importance. In this context, the HGR system plays a vital role by enabling natural and intuitive communication between humans and technology through hand gestures. The presented system uses a single camera and efficient image processing techniques that enable real-time gesture detection. Unlike other methods, our approach employs a basic video camera, which is widely available on most computers, eliminating the need for expensive and specialized hardware. © 2022 Copyright for this paper by its authors.

关键词： Human robot interaction

Sensor Fault Detection and Isolation in Autonomous Nonlinear systems Using Neural Network-Based Observers

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arXiv 2023年

作者： Cao, John Niazi, M. Umar B. Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis- Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor meassurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators. © 2023, CC BY.

关键词： Nonlinear systems

Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

Position-Dependent Snap Feedforward: A Gaussian Process Framework

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arXiv 2022年

作者： van Haren, Max Poot, Maurice Portegies, Jim Oomen, Tom The Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands The Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e. the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward parameter to be modeled as a function of position. Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position. A simulation of a flexible beam shows that a significant performance increase is achieved when using the Gaussian process snap feedforward parameter to compensate for position-dependent compliance. © 2022, CC BY.

关键词： Flexible structures

AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive control

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arXiv 2024年

作者： Reiter, Rudolf Ghezzi, Andrea Baumgärtner, Katrin Hoffmann, Jasper McAllister, Robert D. Diehl, Moritz University of Freiburg Freiburg79110 Germany The Department of Computer Science University of Freiburg Freiburg79110 Germany The Delft Center for Systems and Control Delft University of Technology Delft2628 Netherlands The Department of Microsystems Engineering The Department of Mathematics University of Freiburg Freiburg79110 Germany

Nonlinear model predictive control (MPC) and reinforcement learning (RL) are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic RL techniques can be leveraged to improve the performance of MPC. The RL critic is used as an approximation of the optimal value function, and an actor roll-out provides an initial guess for primal variables of the MPC. A parallel control architecture is proposed where each MPC instance is solved twice for different initial guesses. Besides the actor roll-out initialization, a shifted initialization from the previous solution is used. Thereafter, the actor and the critic are again used to approximately evaluate the infinite horizon cost of these trajectories. The control actions from the lowest-cost trajectory are applied to the system at each time step. We establish that the proposed algorithm is guaranteed to outperform the original RL policy plus an error term that depends on the accuracy of the critic and decays with the horizon length of the MPC formulation. Moreover, we do not require globally optimal solutions for these guarantees to hold. The approach is demonstrated on an illustrative toy example and an automated driving (AD) overtaking scenario. © 2024, CC BY-NC-ND.

关键词： Dynamic programming

Time-Robust Path Planning with Piece-Wise Linear Trajectory for Signal Temporal Logic Specifications

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Time-Robust Path Planning with Piece-Wise Linear Trajectory ...

American control Conference (ACC)

作者： Nhan-Khanh Le Erfaun Noorani Sandra Hirche John S. Baras Chair of Information-oriented Control (ITR) School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Real-world scenarios are characterized by timing uncertainties, e.g., delays, and disturbances. Algorithms with temporal robustness are crucial in guaranteeing the successful execution of tasks and missions in such scenarios. We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Sig-nal Temporal Logic (STL). In contrast to prior approaches that rely on discretized trajectories with fixed time-steps, we leverage Piece-Wise Linear (PWL) signals for the synthesis. PWL signals represent a trajectory through a sequence of time-stamped waypoints. This allows us to encode the STL formula into a Mixed-Integer Linear Program (MILP) with fewer variables. This reduction is more pronounced for specifications with a long planning horizon. To that end, we define time-robustness for PWL signals. Subsequently, we propose quantitative semantics for PWL signals according to the recursive syntax of STL and prove their soundness. We then propose an encoding strategy to transform our semantics into a MILP. Our simulations showcase the soundness and the performance of our algorithm.

关键词： Uncertainty Semantics Transforms Syntactics Encoding Robustness Trajectory

PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

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PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

IEEE Conference on Global Communications (GLOBECOM)

作者： Zihan Li Xiaodong Wang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Finance Operations and Information Systems (FOIS) Brock University Canada Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients’ local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency.

关键词： Training Privacy Data privacy Protocols Federated learning Training data Servers Security Global communication Homomorphic encryption